New Relic announced a series of innovations that operationalize AI across the enterprise, empowering teams to focus on business growth instead of firefighting.
Bolstered by cutting-edge deterministic analytical tools and a suite of new AIOps capabilities, New Relic’s SRE Agent provides next-generation issue triage, change management, incident lifecycle management, intelligent root cause analysis and other features to help engineers cut through data noise and boost operational stability.
“AI is pushing software development beyond human scale, creating a surge of system changes and telemetry volume that IT teams can no longer manage on their own,” said New Relic Chief Product Officer Brian Emerson. “Observability must evolve from simply surfacing data to analyzing it and helping humans take action with less toil. With the new SRE Agent that draws on our powerful AI-strengthened observability capabilities, we’re providing engineers with agentic teammates grounded in live data to resolve incidents faster and with fewer mistakes. The enterprises that win in this era will be those that use AI to cut through noise and optimize business uptime.”
The New Relic SRE Agent helps customers shift operations from reactive to proactive by deploying “always on” AI teammates that diagnose incidents and recommend next steps oftentimes before an engineer acknowledges a page. The agent acts as a specialized worker that performs deep, full-stack diagnostics, combining the flexibility and reasoning capabilities of generative models with “ground truth” brought by a suite of finely-honed deterministic features, such as causal graphs, incident data, performance antipattern knowledge and customer-developed workflows.
The SRE Agent acts as an intelligent context engine for the incident lifecycle. Through Slack and Zoom integrations, responders can query New Relic directly from triage rooms while the SRE Agent captures human context to power automated fact finding, root cause analysis, impact assessments, and reporting. Users gain a unified view of the evolving timeline of events that led up to and following an incident. As a result, they can measure user impact in real-time, identify duplicate incidents, and generate and refine comprehensive post-incident reports.
The New Relic SRE Agent draws on new Intelligent Observability Platform capabilities including:
- Intelligent RCA (iRCA): iRCA cuts through the noise by automatically searching the entity's topology graph, scoring the graph using probabilistic causal models, and applying a path-based ranking algorithm to narrow down the problem space in seconds, not hours. By leveraging iRCA, the SRE Agent performs its most time consuming task —separating noise from signal—in high-confidence, deterministic methodologies.
- Workflow Automation: Intelligent automation that enables teams to automate complex or repetitive operational tasks by creating workflows—structured, multi-step processes that can include conditional logic, human approvals, and integrations with external tools, without writing additional code. DevOps and SREs can improve efficiency by automating everything from notification routing and post-deployment health checks to complex processes such as EC2 instance resizing or Lambda function rollbacks. The SRE Agent will be able to invoke workflows but also be invoked as part of a workflow, which adds an almost endless potential for customization and utility to the mix.
Additional AIOps capabilities now available include:
- Performance Risks Inbox: Goes beyond reactive application performance monitoring (APM) to show why an incident or outage is about to happen so action can quickly be taken. Proactively detects and groups critical coding anti-patterns, including slow SQL queries, N+1 queries, excessive database queries, and the like which threaten application stability and business continuity.
- Smart Alerts: An automated alerting engine that uses AI-strengthened anomaly detection and dynamic baselines to reduce alert noise and improve signal quality across complex environments. By delivering more reliable, behavior-aware alerts, it helps teams respond faster and with greater confidence. Use of the capability also lays the groundwork for businesses to maximize agentic AI, ensuring alerts are automated for better deployment of digital workforces.
These innovations are now available in preview to customers as part of the New Relic Intelligent Observability Platform. Workflow Automation is now generally available.
The Latest
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...